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Abstract Objectives Total tumor volume (TTV) is associated with overall and recurrence-free survival in patients with colorectal cancer liver metastases (CRLM). However, the labor-intensive nature of such manual assessments has hampered the clinical adoption of TTV as an imaging biomarker. This study aimed to develop and externally evaluate a CRLM auto-segmentation model on CT scans, to facilitate the clinical adoption of TTV. Methods We developed an auto-segmentation model to segment CRLM using 783 contrast-enhanced portal venous phase CTs (CT-PVP) of 373 patients. We used a self-learning setup whereby we first trained a teacher model on 99 manually segmented CT-PVPs from three radiologists. The teacher model was then used to segment CRLM in the remaining 663 CT-PVPs for training the student model. We used the DICE score and the intraclass correlation coefficient (ICC) to compare the student model’s segmentations and the TTV obtained from these segmentations to those obtained from the merged segmentations. We evaluated the student model in an external test set of 50 CT-PVPs from 35 patients from the Oslo University Hospital and an internal test set of 21 CT-PVPs from 10 patients from the Amsterdam University Medical Centers. Results The model reached a mean DICE score of 0.85 (IQR: 0.05) and 0.83 (IQR: 0.10) on the internal and external test sets, respectively. The ICC between the segmented volumes from the student model and from the merged segmentations was 0.97 on both test sets. Conclusion The developed colorectal cancer liver metastases auto-segmentation model achieved a high DICE score and near-perfect agreement for assessing TTV. Critical relevance statement AI model segments colorectal liver metastases on CT with high performance on two test sets. Accurate segmentation of colorectal liver metastases could facilitate the clinical adoption of total tumor volume as an imaging biomarker for prognosis and treatment response monitoring. Key Points Developed colorectal liver metastases segmentation model to facilitate total tumor volume assessment. Model achieved high performance on internal and external test sets. Model can improve prognostic stratification and treatment planning for colorectal liver metastases. Graphical Abstract

Abstract Objective This study aimed to develop and evaluate an automatic model using artificial intelligence (AI) for quantifying vascular involvement and classifying tumor resectability stage in patients with pancreatic ductal adenocarcinoma (PDAC), primarily to support radiologists in referral centers. Resectability of PDAC is determined by the degree of vascular involvement on computed tomography scans (CTs), which is associated with considerable inter-observer variability. Methods We developed a semisupervised machine learning segmentation model to segment the PDAC and surrounding vasculature using 613 CTs of 467 patients with pancreatic tumors and 50 control patients. After segmenting the relevant structures, our model quantifies vascular involvement by measuring the degree of the vessel wall that is in contact with the tumor using AI-segmented CTs. Based on these measurements, the model classifies the resectability stage using the Dutch Pancreatic Cancer Group criteria as either resectable, borderline resectable, or locally advanced (LA). Results We evaluated the performance of the model using a test set containing 60 CTs from 60 patients, consisting of 20 resectable, 20 borderline resectable, and 20 locally advanced cases, by comparing the automated analysis obtained from the model to expert visual vascular involvement assessments. The model concurred with the radiologists on 227/300 (76%) vessels for determining vascular involvement. The model’s resectability classification agreed with the radiologists on 17/20 (85%) resectable, 16/20 (80%) for borderline resectable, and 15/20 (75%) for locally advanced cases. Conclusions This study demonstrates that an AI model may allow automatic quantification of vascular involvement and classification of resectability for PDAC. Relevance statement This AI model enables automated vascular involvement quantification and resectability classification for pancreatic cancer, aiding radiologists in treatment decisions, and potentially improving patient outcomes. Key points • High inter-observer variability exists in determining vascular involvement and resectability for PDAC. • Artificial intelligence accurately quantifies vascular involvement and classifies resectability for PDAC. • Artificial intelligence can aid radiologists by automating vascular involvement and resectability assessments. Graphical Abstract

Background:. Abdominal computed tomography (CT) is the standard imaging modality for detection and staging in patients with colorectal liver metastases (CRLM). Although liver magnetic resonance imaging (MRI) is superior to CT in detecting small lesions, guidelines are ambiguous regarding the added value of an additional liver MRI in the surgical workup of patients with CRLM. Therefore, this systematic review and meta-analysis aimed to evaluate the clinical added value of liver MRI in patients eligible for resection or ablation of CRLM based on CT. Methods:. A systematic search was performed in the PubMed, Embase, and Cochrane Library databases through June 23, 2023. Studies investigating the impact of additional MRI on local treatment plan following CT in patients with CRLM were included. Risk of bias was assessed using the QUADAS-2 tool. The pooled weighted proportions for the primary outcome were calculated using random effect meta-analysis. Results:. Overall, 11 studies with 1440 patients were included, of whom 468 patients (32.5%) were assessed for change in local treatment plan. Contrast-enhanced liver MRI was used in 10 studies, including gadoxetic acid in 9 studies. Liver MRI with diffusion-weighted imaging was used in 8 studies. Pooling of data found a 24.12% (95% confidence interval, 15.58%–32.65%) change in the local treatment plan based on the added findings of liver MRI following CT. Sensitivity analysis including 5 studies (268 patients) focusing on monophasic portal venous CT followed by gadoxetic acid-enhanced liver MRI with diffusion-weighted imaging showed a change of local treatment plan of 17.88% (95% confidence interval, 5.14%–30.62%). Conclusions:. This systematic review and meta-analysis found that liver MRI changed the preinterventional local treatment plan in approximately one-fifth of patients eligible for surgical resection or ablation of CRLM based on CT. These findings suggest a clinically relevant added value of routine liver MRI in the preinterventional workup of CRLM, which should be confirmed by large prospective studies.

Background: Cancer induced bone pain (CIBP) strongly interferes with patient’s quality of life. Currently, the standard of care includes external beam radiotherapy (EBRT), resulting in pain relief in approximately 60% of patients. Magnetic Resonance guided High Intensity Focused Ultrasound (MR-HIFU) is a promising treatment modality for CIBP. Methods: A single arm, R-IDEAL stage I/IIa study was conducted. Patients presenting at the department of radiation oncology with symptomatic bone metastases in the appendicular skeleton, as well as in the sacrum and sternum were eligible for inclusion. All participants underwent EBRT, followed by MR-HIFU within 4 days. Safety and feasibility were assessed, and pain scores were monitored for 4 weeks after completing the combined treatment. Results: Six patients were enrolled. Median age was 67 years, median lesion diameter was 56,5 mm. In all patients it was logistically possible to plan and perform the MR-HIFU treatment within 4 days after EBRT. All patients tolerated the combined procedure well. Pain response was reported by 5 out of 6 patients at 7 days after completion of the combined treatment, and stabilized on 60% at 4 weeks follow up. No treatment related serious adverse events occurred. Conclusion: This is the first study to combine EBRT with MR-HIFU. Our results show that combined EBRT and MR-HIFU in first-line treatment of CIBP is safe and feasible, and is well tolerated by patients. Superiority over standard EBRT, in terms of (time to) pain relief and quality of life need to be evaluated in comparative (randomized) study.

Purpose: We investigated whether administration of the long-acting uterus stimulant carbetocin increased intra-subject sonication efficiency during Magnetic Resonance image guided High Intensity Focused Ultrasound (MR-HIFU) treatment of uterine fibroids. Method: In this prospective cohort study, thirty women with symptomatic uterine fibroids undergoing MR-HIFU treatment were included between January 2018 and January 2019. Treatment started with three sonications on one side of the uterine fibroid. Subsequently, one ampoule of 1 mL carbetocin (100 µg/mL) was administered intravenously and treatment continued with three sonications on the other side of the uterine fibroid. We compared the intra-subject sonication efficiency, in terms of Energy Efficiency Factor (EEF), thermal dose volume and sonication time to ablate one cm3 of fibroid tissue, before and after carbetocin administration. Adverse events that occurred within 30 min after carbetocin administration were recorded. Results: Sonication efficiency improved after carbetocin administration as indicated by a significant decrease in EEF and sonication time (p = 0.006 and p = 0.001 respectively), and a significant increase in thermal dose volume reached (p =

A 34-year-old woman entered the emergency room with abdominal pain in the right upper quadrant. Computed tomography scan showed a nutmeg liver suspected for increased venous pressure by thrombosis of the liver veins, Budd-Chiari malformation, or right-sided heart failure. Interestingly, the diagnosis was pelvic inflammatory disease complicated by the Fitz-Hugh-Curtis syndrome (FHCS).Pelvic inflammatory disease resulted from an ascended infection by Chlamydia trachomatis. FHCS was caused by perihepatitis defined as inflammation of the peritoneal capsule of the liver. Fast diagnosis and treatment is crucial. Therefore, we report a case of FHCS characterized by a nutmeg liver on computed tomography. Keywords: Fitz-Hugh-Curtis syndrome, Perihepatitis, Hepatomegaly, Nutmeg liver, Pelvic Inflammatory disease, Chlamydia trachomatis

BackgroundMagnetic resonance image–guided high-intensity–focused ultrasound (MR-HIFU) is a rather new, noninvasive option for the treatment of uterine fibroids. It is safe, effective, and has a very short recovery time. However, a lack of prospectively collected data on long-term (cost-)effectiveness of the MR-HIFU treatment compared with standard uterine fibroid care prevents the MR-HIFU treatment from being reimbursed for this indication. Therefore, at this point, when conservative treatment for uterine fibroid symptoms has failed or is not accepted by patients, standard care includes the more invasive treatments hysterectomy, myomectomy, and uterine artery embolization (UAE). Primary outcomes of currently available data on MR-HIFU treatment often consist of technical outcomes, instead of patient-centered outcomes such as quality of life (QoL), and do not include the use of the latest equipment or most up-to-date treatment strategies. Moreover, data on cost-effectiveness are rare and seldom include data on a societal level such as productivity loss or use of painkillers. Because of the lack of reimbursement, broad clinical implementation has not taken place, nor is the proper role of MR-HIFU in uterine fibroid care sufficiently clear. ObjectiveThe objective of our study is to determine the long-term (cost-)effectiveness of MR-HIFU compared with standard (minimally) invasive fibroid treatments. MethodsThe MYCHOICE study is a national, multicenter, open randomized controlled trial with randomization in a 2:1 ratio to MR-HIFU or standard care including hysterectomy, myomectomy, and UAE. The sample size is 240 patients in total. Women are included when they are 18 years or older, in premenopausal stage, diagnosed with symptomatic uterine fibroids, conservative treatment has failed or is not accepted, and eligible for MR-HIFU. Primary outcomes of the study are QoL 24 months after treatment and costs of treatment including direct health care costs, loss of productivity, and patient costs. ResultsInclusion for the MYCHOICE study started in November 2020 and enrollment will continue until 2024. Data collection is expected to be completed in 2026. ConclusionsBy collecting data on the long-term (cost-)effectiveness of the MR-HIFU treatment in comparison to current standard fibroid care, we provide currently unavailable evidence about the proper place of MR-HIFU in the fibroid treatment spectrum. This will also facilitate reimbursement and inclusion of MR-HIFU in (inter)national uterine fibroid care guidelines. Trial RegistrationNetherlands Trial Register NL8863; https://www.trialregister.nl/trial/8863 International Registered Report Identifier (IRRID)DERR1-10.2196/29467